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Field service organizations are always facing significant challenges when it comes to improving both efficiency and customer experience. With the introduction of Artificial Intelligence for field service, many of these challenges are overcome, as we’ll explore in this article.
In today’s customer-centric and highly competitive marketplace, organizations providing field service must meet growing demand for shorter waiting times, quicker resolutions, and better overall customer experience. Failure to achieve a first time fix means more downtime for the customer, another tech dispatch and lower satisfaction ratings. Technicians are therefore expected to arrive fully prepared, armed with knowledge about the history of the problem and any previous work done on site, as well as the proper parts and tools necessary to get the job done – on the first visit.
The Power of Artificial Intelligence in Field Service
Technicians must therefore be aware of any previous attempts at remote diagnosis and have access to knowledge base articles and videos, as well as the ability to collaborate with senior colleagues. Thanks to Artificial Intelligence (AI), field service organizations can now make all this a reality. In this article we’ll explore how three practical and innovative applications – AI-driven scheduling, AI-powered knowledge bases and AI-based visual analysis – are driving an AI field service revolution.
AI-Driven Scheduling for Field Service
Human schedulers at field service organizations must keep on top of multiple technicians, assessing their availability and skill sets for each job. Human error, such as double booking and misassignment are often inevitable, as are job overruns and cancellations. AI technology overcomes the hurdles facing manual dispatchers by automatically assigning jobs to the right technicians based on priority and various other factors such as their:
- history
- skills
- location
- tools
- availability
AI-Powered Knowledge Base for Field Service Technicians
Many enterprises have invested heavily in their knowledge bases in order to provide opportunities for technicians to “self-help,” especially when remote supervisors are struggling with an overload of requests for advice. Sometimes, however, technicians or experts simply cannot find the information they seek in a timely manner. Next-generation AI assistants are now emerging to help field service technicians and experts find the solutions they need from the company knowledge base using natural language processing (NLP). These assistants can process voice requests from the technician and provide the support they require. Examples of how technicians can use AI assistants include:
- ordering new parts,
- rescheduling a service call
- confirming that the next customer is at home
AI-based Visual Analysis by Remote Experts
Technicians are often dependent on remote experts for specific aspects of the job, such as authorization, remote guidance, quality control and safety. The expert has only one pair of eyes – a fact that often causes workflow bottlenecks which delay job completion. Automating these processes using visual analysis tools can be a game-changer, thanks to Computer Vision AI, which helps the remote expert see the technician’s issue or the customer’s problem, driving efficiencies in the resolution process. Computer Vision AI also enables the field service technician to perform a variety of tasks autonomously using a virtual visual assistant.
Find out more about Artificial Intelligence in Field Service
For a deeper dive into these transformative applications, download TechSee’s new eBook, Artificial Intelligence: A New Frontier for Field Service which is crammed with use cases and practical examples of AI in field service. The resource explores the importance of field service AI, including how it drives efficiencies and powers productivity with knowledge management tools and data analytics. It explains how enterprises can lower operational costs by ensuring that they dispatch the right person with the right skills and the right parts every time.
With 70% of organizations concerned about knowledge loss from a retiring workforce, it also details how organizations can close the generational gap by ensuring successful knowledge sharing between veteran technicians and novice employees.
